Identification of long-term trends in vegetation dynamics in the Guinea savannah region of Nigeria

Abstract

The availability of newly generated data from Advanced Very High Resolution Radiometer (AVHRR) covering the last three decades has broaden our understanding of vegetation dynamics (greening) from global to regional scale through quantitative analysis of seasonal trends in vegetation time series and climatic variability especially in the Guinea savannah region of Nigeria where greening trend is inconsistent. Due to the impact of changes in global climate and sustainability of means of human livelihood, increasing interest on vegetation productivity has become important. The aim of this study is to examine association between NDVI and rainfall using remotely sensed data, since vegetation dynamics (greening) has a high degree of association with weather parameters. This study therefore analyses trends in regional vegetation dynamics in Kogi state, Nigeria using bi-monthly AVHRR GIMMS 3g (Global Inventory Modelling and Mapping Studies) data and TAMSAT (Tropical Applications of Meteorology Satellite) monthly data both from 1983 to 2011 to identify changes in vegetation greenness over time. Analysis of changes in the seasonal variation of vegetation greenness and climatic drivers was conducted for selected locations to further understand the causes of observed interannual changes in vegetation dynamics. For this study, Mann-Kendall (MK) monotonic method was used to analyse long-term inter-annual trends of NDVI and climatic variable. The Theil-Sen median slope was used to calculate the rate of change in slopes between all pair wise combination and then assessing the median over time. Trends were also analysed using a linear model method, after seasonality had been removed from the original NDVI and rainfall data. The result of the linear model are statistically significant (p <0.01) in all the study location which can be interpreted as increase in vegetation trend over time (greening). Also the result of the NDVI trend analysis using Mann-Kendall test shows an increasing (i.e. positive) trend in the time series. The significance of the result was tested using Kendall's tau rank correlation coefficient and the results were significant. Finally the NDVI data and TAMSAT data were analysed together in order to describe the relationship between both values. Although, increase in rainfall over the last decades enhances vegetation greenness, other factors such as land use change and population density need to be investigated in order to better explain changing trends of vegetation greening for the study area in the future.

Coastal ecosystem monitoring using long-term satellite data records: A case study of Chilika Lake, Odisha

“…Theil-Sen method is a robust non-parametric statistical operator which is used to analyse trends (Osunmadewa B.A., 2014) and it is determined by calculating paired slopes, from every point in In final process all datasets were stacked to make a one temporal image and trend were calculated for each multi-spectral index in the temporal domain by using robust Theil-Sen (T-S) regression method (Sen, 1968;Theil, 1992). The T-S regression method was applied on these spectral indices to calculate trend parameters such as slope and pvalue.…”

“…Trend analysis refers to rate of change in times series analysis and results are often similar to ordinary linear regression which was used for long term series analysis (Osunmadewa B.A. et.…”

Hydro-climatic and Land use/cover Changes in Nasia Catchment of the White Volta Basin in Ghana

“…At the same time, it has low sensitivity to abrupt breaks due to inhomogeneous time series (Tabari et al, 2011). This test has been extensively and successfully used to detect trends in hydro-climatic studies (Xu et al, 2010;Sun et al, 2013;Zhang et al, 2015;Mwangi et al, 2016) and NDVI (Forkel et al, 2013;Osunmadewa et al, 2014).…”